What is the purpose of the 'k' in k-Nearest Neighbors (kNN) algorithm?
- It indicates the number of features in the dataset
- It is the dimensionality of the input space
- It represents the number of clusters in the dataset
- It signifies the number of nearest neighbors to consider
The 'k' in k-Nearest Neighbors refers to the number of nearest neighbors to consider when making predictions. A higher 'k' leads to a smoother decision boundary, while a lower 'k' makes the algorithm more sensitive to local patterns.
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